Traditionally, the method for diagnosing a malfunctioning cooling device is to follow a series of steps in diagnosis. Failures would be categorized, then organized into a list where the end user would find the specific symptom that the unit exhibits. Then the user would reference the symptom to the corresponding list of checks and possible problems. For example, if the freezer is not operating at an optimal temperature, the user may be instructed to check the condenser coils, the door seals, test the temperature control, and check for a refrigerant leak, etc.
The problem with this method is twofold. First, the user is limited to diagnosing failures that have already occurred rather than proactively addressing mechanical issues before they manifest symptoms. This diagnosis is generally limited to determining a mechanical issue by a change in cooling temperature. Second, diagnosis is dependent on the user having broad knowledge of the unit's operating components. While diagnosis may be a time-consuming effort if the user has broad knowledge, it will be particularly difficult for a user who is uninformed.
As the end user has additional cooling needs the addition of new units becomes necessary. This increases the likelihood that additional model types or brands may be used. This multiplies the amount of necessary information the user must know to successfully diagnose and repair issues and substantially increases the difficulty of diagnosis.
What is needed is a device that can intelligently record the performance characteristics of a cooling device and allow for the diagnosis of performance degradation or failure based on this data. Proactively addressing these issues is likely to save repair costs, prevent loss of service to the customer thereby allowing the user uninterrupted business, and finally to prevent spoilage.